
import geopandas as pd
import contextily as ctx # Used for contextual basemaps
import matplotlib.pyplot as plt
from geocube.api.core import make_geocube # Used for rasterizing
import os
import shapely
import imageio # Used for making animated GIFs
import numpy as np
from IPython.display import Image
from osgeo import gdal # Raster operations
plt.rcParams['figure.figsize'] = (20, 20)
os.listdir("input")

/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
  warnings.warn(

['lds-nz-road-centrelines-topo-150k-FGDB.zip',
 'lds-nz-8m-digital-elevation-model-2012-GTiff-auckland-region.zip',
 'statsnzpopulation-by-meshblock-2013-census-FGDB.zip',
 'statsnz2018-census-electoral-population-meshblock-2020-FGDB.zip',
 'statsnzregional-council-2021-clipped-generalised-FGDB.zip',
 'lris-lcdb-v50-land-cover-database-version-50-mainland-new-zealand-FGDB.zip']


%%time
REGC = pd.read_file("input/statsnzregional-council-2021-clipped-generalised-FGDB.zip!regional-council-2021-clipped-generalised.gdb")
AKL = REGC[REGC.REGC2021_V1_00_NAME == "Auckland Region"].copy()
# Filter out islands
AKL["geometry"] = max(AKL.geometry.explode(), key=lambda a: a.area)
# Coordinate reference system (projection)
print(AKL.crs)
# Simplify geometry to speed up clip operations
AKL = AKL.simplify(1000).buffer(1000)
ax = AKL.to_crs(epsg=3857).boundary.plot()
ax.set_title("Auckland Region clip extent")
ctx.add_basemap(ax)

epsg:2193
CPU times: user 1.78 s, sys: 935 ms, total: 2.71 s
Wall time: 14.4 s


%%time
df = pd.read_file("zip://input/lris-lcdb-v50-land-cover-database-version-50-mainland-new-zealand-FGDB.zip!lcdb-v50-land-cover-database-version-50-mainland-new-zealand.gdb")

CPU times: user 1min 47s, sys: 2.5 s, total: 1min 49s
Wall time: 1min 49s


print(df.columns)
print(df.crs)
display(df.sample(5))

Index(['Name_2018', 'Name_2012', 'Name_2008', 'Name_2001', 'Name_1996',
       'Class_2018', 'Class_2012', 'Class_2008', 'Class_2001', 'Class_1996',
       'Wetland_18', 'Wetland_12', 'Wetland_08', 'Wetland_01', 'Wetland_96',
       'Onshore_18', 'Onshore_12', 'Onshore_08', 'Onshore_01', 'Onshore_96',
       'EditAuthor', 'EditDate', 'LCDB_UID', 'geometry'],
      dtype='object')
epsg:2193


%%time
df = pd.clip(df, AKL)

CPU times: user 46.6 s, sys: 10.2 ms, total: 46.6 s
Wall time: 46.6 s


df.sample(5)


df.Name_2018.value_counts()

Exotic Forest                                3981
Indigenous Forest                            3673
Manuka and/or Kanuka                         2282
Broadleaved Indigenous Hardwoods             1788
Built-up Area (settlement)                   1350
High Producing Exotic Grassland              1326
Mangrove                                     1151
Urban Parkland/Open Space                    1099
Estuarine Open Water                          441
Orchard, Vineyard or Other Perennial Crop     436
Short-rotation Cropland                       362
Lake or Pond                                  326
Herbaceous Saline Vegetation                  303
Low Producing Grassland                       291
Gorse and/or Broom                            287
Forest - Harvested                            266
Sand or Gravel                                252
Deciduous Hardwoods                           201
Surface Mine or Dump                          132
Mixed Exotic Shrubland                        120
Herbaceous Freshwater Vegetation              118
Transport Infrastructure                      107
River                                          15
Gravel or Rock                                  9
Flaxland                                        9
Fernland                                        2
Matagouri or Grey Scrub                         1
Name: Name_2018, dtype: int64


def simplify_classes(code):
    if code in [1, 2, 5]:
        return 1, "Urban"
    elif code in [68,69,71]:
        return 2, "Vegetation"
    elif code in [0,20,21,22,45,46]:
        return 3, "Water"
    else:
        return 4, "Other"

summary = []
years = [1996, 2001, 2008, 2012, 2018]
for year in years:
    print(year)
    class_year = f"Class_{year}"
    df[class_year + "_simplified_code"] = df[class_year].apply(lambda c: simplify_classes(c)[0])
    df[class_year + "_simplified_name"] = df[class_year].apply(lambda c: simplify_classes(c)[1])
    summary.append(df[class_year + "_simplified_name"].value_counts())

1996
2001
2008
2012
2018


pd.GeoDataFrame(summary).plot.area()

<AxesSubplot:>


%%capture
# %%capture suppresses output
ims = []
years = [1996, 2001, 2008, 2012, 2018]
for year in years:
    ax = df.plot(column=f'Class_{year}_simplified_name', legend=True)
    ax.set_title(year)
    ax.figure.tight_layout()
    canvas = ax.figure.canvas
    canvas.draw() # draw the canvas, cache the renderer
    image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
    image = image.reshape(canvas.get_width_height()[::-1] + (3,))
    ims.append(image)

imageio.mimsave("land_use.gif", ims, fps=1)


with open('land_use.gif','rb') as file:
    display(Image(file.read()))


cols = [f"Class_{year}_simplified_code" for year in years]
cols

['Class_1996_simplified_code',
 'Class_2001_simplified_code',
 'Class_2008_simplified_code',
 'Class_2012_simplified_code',
 'Class_2018_simplified_code']


%%time
geocube = make_geocube(
    vector_data=df,
    output_crs="epsg:2193",
    measurements=cols,
    resolution=(-100, 100),
    fill=0, # NaNs, like offshore areas, will be 0
)
geocube

CPU times: user 20.3 s, sys: 9.94 ms, total: 20.3 s
Wall time: 20.3 s

<xarray.Dataset>
Dimensions:                     (x: 1001, y: 1320)
Coordinates:
  * y                           (y) float64 6.002e+06 6.002e+06 ... 5.87e+06
  * x                           (x) float64 1.704e+06 1.704e+06 ... 1.804e+06
    spatial_ref                 int64 0
Data variables:
    Class_1996_simplified_code  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    Class_2001_simplified_code  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    Class_2008_simplified_code  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    Class_2012_simplified_code  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    Class_2018_simplified_code  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    grid_mapping:  spatial_ref

array([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])

array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])

array(0)

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])


geocube.Class_2018_simplified_code.plot()

<matplotlib.collections.QuadMesh at 0x7ff56e199a00>


for year in years:
    print(year)
    outfile = f"output/land_use_{year}.tif"
    if not os.path.isfile(outfile):
        geocube[f"Class_{year}_simplified_code"].rio.to_raster(outfile, dtype=np.byte) # Use np.byte for smaller output filesize

1996
2001
2008
2012
2018


%%time
pop2013 = pd.read_file("input/statsnzpopulation-by-meshblock-2013-census-FGDB.zip!population-by-meshblock-2013-census.gdb")

CPU times: user 9.29 s, sys: 10.3 ms, total: 9.3 s
Wall time: 9.29 s


%%time
pop2013 = pd.clip(pop2013, AKL)

CPU times: user 19.6 s, sys: 0 ns, total: 19.6 s
Wall time: 19.6 s


display(pop2013.sample(5))


#pop2013.Population_Count_Usual_Resident_2013.replace(0, np.nan, inplace=True)


pop2013.Population_Count_Usual_Resident_2013.plot(kind="hist", bins=200)

<AxesSubplot:ylabel='Frequency'>


%%time
pop2013_cube = make_geocube(
    vector_data=pop2013,
    measurements=["Population_Count_Usual_Resident_2013"],
    like=geocube, # Ensures dimensions match
    fill=0 # NaNs, like offshore areas, will be 0
)
pop2013_cube

CPU times: user 2.74 s, sys: 0 ns, total: 2.74 s
Wall time: 2.74 s

<xarray.Dataset>
Dimensions:                               (x: 1001, y: 1320)
Coordinates:
  * y                                     (y) float64 6.002e+06 ... 5.87e+06
  * x                                     (x) float64 1.704e+06 ... 1.804e+06
    spatial_ref                           int64 0
Data variables:
    Population_Count_Usual_Resident_2013  (y, x) float64 0.0 0.0 0.0 ... 0.0 0.0
Attributes:
    grid_mapping:  spatial_ref

array([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])

array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])

array(0)

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])


pop2013_cube.Population_Count_Usual_Resident_2013.plot()
outfile = "output/pop2013.tif"
if not os.path.isfile(outfile):
    # byte max value is 255, and we have larger values than that here. uint16 max value is 65535, which is fine
    pop2013_cube.Population_Count_Usual_Resident_2013.rio.to_raster(outfile, dtype=np.uint16)


%%time
pop2018 = pd.read_file("input/statsnz2018-census-electoral-population-meshblock-2020-FGDB.zip!2018-census-electoral-population-meshblock-2020.gdb")

CPU times: user 10 s, sys: 80 ms, total: 10.1 s
Wall time: 10.1 s


%%time
pop2018 = pd.clip(pop2018, AKL)

CPU times: user 21.4 s, sys: 0 ns, total: 21.4 s
Wall time: 21.4 s


display(pop2018.sample(5))


pop2018.General_Electoral_Population.replace(-999, 0, inplace=True)


pop2018.General_Electoral_Population.plot(kind="hist", bins=200)

<AxesSubplot:ylabel='Frequency'>


%%time
pop2018_cube = make_geocube(
    vector_data=pop2018,
    measurements=["General_Electoral_Population"],
    like=geocube, # Ensures dimensions match
    fill=0
)
pop2018_cube

CPU times: user 2.53 s, sys: 223 µs, total: 2.53 s
Wall time: 2.53 s

<xarray.Dataset>
Dimensions:                       (x: 1001, y: 1320)
Coordinates:
  * y                             (y) float64 6.002e+06 6.002e+06 ... 5.87e+06
  * x                             (x) float64 1.704e+06 1.704e+06 ... 1.804e+06
    spatial_ref                   int64 0
Data variables:
    General_Electoral_Population  (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    grid_mapping:  spatial_ref

array([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])

array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])

array(0)

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])


pop2018_cube.General_Electoral_Population.plot()
outfile = "output/pop2018.tif"
if not os.path.isfile(outfile):
    pop2018_cube.General_Electoral_Population.rio.to_raster(outfile, dtype=np.uint16)


%%time
roads = pd.read_file("input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb")

CPU times: user 11.6 s, sys: 20 ms, total: 11.7 s
Wall time: 11.7 s


%%time
akl_roads = pd.clip(roads, AKL)

CPU times: user 23.7 s, sys: 0 ns, total: 23.7 s
Wall time: 23.7 s


# If a road has a highway number (hway_num not None), it's a highway/motorway
mway = akl_roads[~akl_roads.hway_num.isna()].copy()
mway


mway.name.value_counts().head(50).plot(kind="barh").invert_yaxis()


mway.hway_num.value_counts().head(50).plot(kind="barh").invert_yaxis()


ax = mway.to_crs(epsg=3857).plot()
ax.set_title("Auckland Region motorways")
ctx.add_basemap(ax)


%%time
mway_cube = make_geocube(
    vector_data=mway,
    measurements=["lane_count"],
    like=geocube, # Ensures dimensions match
    fill=0, # 0 works fine here, as every mway has at least one lane
)
mway_cube

CPU times: user 220 ms, sys: 10 ms, total: 230 ms
Wall time: 228 ms

<xarray.Dataset>
Dimensions:      (x: 1001, y: 1320)
Coordinates:
  * y            (y) float64 6.002e+06 6.002e+06 ... 5.871e+06 5.87e+06
  * x            (x) float64 1.704e+06 1.704e+06 ... 1.804e+06 1.804e+06
    spatial_ref  int64 0
Data variables:
    lane_count   (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
Attributes:
    grid_mapping:  spatial_ref

array([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])

array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])

array(0)

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])


mway_cube.lane_count.plot()
outfile = "output/mway.tif"
if not os.path.isfile(outfile):
    mway_cube.lane_count.rio.to_raster(outfile, dtype=np.byte)


src_ds = gdal.Open("output/mway.tif")
srcband = src_ds.GetRasterBand(1)
dst_filename = "output/mway_dist.tif"
drv = gdal.GetDriverByName('GTiff')
dst_ds = drv.Create( dst_filename,
                     src_ds.RasterXSize, src_ds.RasterYSize, 1,
                     gdal.GetDataTypeByName('UInt16'))
dst_ds.SetGeoTransform( src_ds.GetGeoTransform() )
dst_ds.SetProjection( src_ds.GetProjectionRef() )
dstband = dst_ds.GetRasterBand(1)
prox = gdal.ComputeProximity(srcband,dstband,["DISTUNITS=GEO"]) # Encoded value is distance from motorway in meters
# Garbage collection of this variable flushes write
dst_ds = None
dst_ds = gdal.Open(dst_filename)
mway_dist = np.array(dst_ds.GetRasterBand(1).ReadAsArray())
print(mway_dist.shape)
plt.imshow(mway_dist)
plt.title("Distance from motorways in Auckland")
cb = plt.colorbar()
cb.ax.set_title("Distance (m)")

(1320, 1001)

Text(0.5, 1.0, 'Distance (m)')


!ls -Ggh output

total 15M
-rw-r--r-- 1 1.3M Apr 15 14:25 land_use_1996.tif
-rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2001.tif
-rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2008.tif
-rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2012.tif
-rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2018.tif
-rw-r--r-- 1 1.3M Apr 15 14:37 mway.tif
-rw-r--r-- 1 2.6M Apr 15 15:16 mway_dist.tif
-rw-r--r-- 1 2.6M Apr 15 14:31 pop2013.tif
-rw-r--r-- 1 2.6M Apr 15 14:32 pop2018.tif

	Meshblock	MeshblockNumber	Population_Count_Usual_Resident_2013	Population_Count_Census_Night_2013	geometry
15957	MB 0518100	0518100	192	198	POLYGON ((1753302.613 5915284.124, 1753324.337...
18251	MB 0705100	0705100	135	135	POLYGON ((1767892.819 5907131.876, 1767820.019...
15659	MB 0494806	0494806	81	81	POLYGON ((1761809.284 5915869.657, 1761809.813...
20092	MB 0798102	0798102	102	96	POLYGON ((1773963.418 5896156.722, 1773973.320...
14566	MB 0389602	0389602	252	249	POLYGON ((1750597.782 5916672.714, 1750598.204...

	MB2020_V2_00	General_Electoral_Population	Maori_Electoral_Population	GED2020_V1_00	GED2020_V1_00_NAME	GED2020_V1_00_NAME_ASCII	MED2020_V1_00	MED2020_V1_00_NAME	MED2020_V1_00_NAME_ASCII	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry
19080	0767911	168	18	048	Takanini	Takanini	3	Tāmaki Makaurau	Tamaki Makaurau	0.076304	0.076304	1842.690356	POLYGON ((1769823.781 5899194.914, 1769879.451...
914	0217601	57	-999	018	Kaipara ki Mahurangi	Kaipara ki Mahurangi	5	Te Tai Tokerau	Te Tai Tokerau	5.475235	5.475235	12795.361157	POLYGON ((1736671.915 5938305.093, 1736780.648...
12252	0177701	120	-999	018	Kaipara ki Mahurangi	Kaipara ki Mahurangi	5	Te Tai Tokerau	Te Tai Tokerau	4.417407	4.417407	16595.345385	POLYGON ((1745943.515 5950593.427, 1746045.393...
18285	0713003	204	21	004	Botany	Botany	3	Tāmaki Makaurau	Tamaki Makaurau	0.031940	0.031940	938.854375	POLYGON ((1768777.247 5906637.326, 1768772.926...
48900	4006511	123	-999	011	Epsom	Epsom	3	Tāmaki Makaurau	Tamaki Makaurau	0.029792	0.029792	1084.495229	POLYGON ((1761006.443 5917698.252, 1760991.042...

	t50_fid	name_ascii	macronated	name	hway_num	rna_sufi	lane_count	way_count	status	surface	geometry
512	100120610	KAIPARA COAST HIGHWAY	N	KAIPARA COAST HIGHWAY	16	3007739	2	None	None	sealed	LINESTRING (1732000.000 5944172.070, 1732048.5...
2933	3198057	STATE HIGHWAY 1	N	STATE HIGHWAY 1	1	3027695	2	None	None	sealed	LINESTRING (1748581.508 5968975.145, 1748558.4...
2934	3198059	STATE HIGHWAY 1	N	STATE HIGHWAY 1	1	3027695	2	None	None	sealed	LINESTRING (1748171.047 5971284.152, 1748129.9...
3320	3200754	PAERATA ROAD	N	PAERATA ROAD	22	3000260	2	None	None	sealed	LINESTRING (1767236.112 5888088.508, 1767244.3...
3324	3200792	UPPER HARBOUR MOTORWAY	N	UPPER HARBOUR MOTORWAY	18	3047073	4	None	None	sealed	LINESTRING (1747954.314 5927269.837, 1747970.0...
...	...	...	...	...	...	...	...	...	...	...	...
138240	100048291	AUCKLAND-WAIWERA MOTORWAY	N	AUCKLAND-WAIWERA MOTORWAY	1	3067966	7	None	None	sealed	LINESTRING (1755881.018 5922863.734, 1755886.4...
138301	100048432	AUCKLAND-HAMILTON MOTORWAY	N	AUCKLAND-HAMILTON MOTORWAY	1	3017109	1	None	None	sealed	LINESTRING (1765115.647 5909916.697, 1765092.7...
138337	100048532	STATE HIGHWAY 1	N	STATE HIGHWAY 1	1	3027695	4	None	None	sealed	LINESTRING (1748892.089 5949596.727, 1748892.0...
138369	100048589	PORT ALBERT ROAD	N	PORT ALBERT ROAD	16	3013274	2	None	None	sealed	LINESTRING (1734173.019 5980575.187, 1734175.6...
138680	100118365	SOUTH-WESTERN MOTORWAY	N	SOUTH-WESTERN MOTORWAY	20	3018532	4	None	None	sealed	LINESTRING (1760066.252 5908184.133, 1760043.9...

Land use¶

Population density¶

Distance from major roads¶

Distance from CBD¶

	Name_2018	Name_2012	Name_2008	Name_2001	Name_1996	Class_2018	Class_2012	Class_2008	Class_2001	Class_1996	...	Wetland_96	Onshore_18	Onshore_12	Onshore_08	Onshore_01	Onshore_96	EditAuthor	EditDate	LCDB_UID	geometry
122856	Manuka and/or Kanuka	Manuka and/or Kanuka	Manuka and/or Kanuka	Manuka and/or Kanuka	Manuka and/or Kanuka	52	52	52	52	52	...	no	yes	yes	yes	yes	yes	Terralink	2004-06-30T00:00:00	lcdb1000090321	MULTIPOLYGON (((1764675.184 5789790.017, 17646...
64325	Broadleaved Indigenous Hardwoods	Broadleaved Indigenous Hardwoods	Broadleaved Indigenous Hardwoods	Broadleaved Indigenous Hardwoods	Broadleaved Indigenous Hardwoods	54	54	54	54	54	...	no	yes	yes	yes	yes	yes	Terralink	2004-06-30T00:00:00	lcdb2000139449	MULTIPOLYGON (((1496215.025 5264808.673, 14963...
491051	Lake or Pond	Lake or Pond	Lake or Pond	Lake or Pond	Lake or Pond	20	20	20	20	20	...	no	yes	yes	yes	yes	yes	Landcare Research	2004-06-30T00:00:00	lcdb2000044898	MULTIPOLYGON (((1209708.330 4902000.847, 12096...
477442	Exotic Forest	Exotic Forest	Exotic Forest	Exotic Forest	Exotic Forest	71	71	71	71	71	...	no	yes	yes	yes	yes	yes	Landcare Research	2011-06-30T00:00:00	lcdb2000210008	MULTIPOLYGON (((1422892.426 5039258.375, 14228...
27336	Matagouri or Grey Scrub	Matagouri or Grey Scrub	Matagouri or Grey Scrub	Matagouri or Grey Scrub	Matagouri or Grey Scrub	58	58	58	58	58	...	no	yes	yes	yes	yes	yes	Landcare Research	2011-06-30T00:00:00	lcdb2000163359	MULTIPOLYGON (((1314716.722 4950010.195, 13146...

	Name_2018	Name_2012	Name_2008	Name_2001	Name_1996	Class_2018	Class_2012	Class_2008	Class_2001	Class_1996	...	Wetland_96	Onshore_18	Onshore_12	Onshore_08	Onshore_01	Onshore_96	EditAuthor	EditDate	LCDB_UID	geometry
391151	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	69	69	69	69	69	...	no	yes	yes	yes	yes	yes	Terralink	2004-06-30T00:00:00	lcdb1000163624	POLYGON ((1750839.814 5875665.073, 1750863.262...
377272	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	69	69	69	69	69	...	no	yes	yes	yes	yes	yes	Terralink	2004-06-30T00:00:00	lcdb1000168029	POLYGON ((1746126.136 5990128.894, 1746087.278...
458974	Herbaceous Freshwater Vegetation	Herbaceous Freshwater Vegetation	Herbaceous Freshwater Vegetation	Herbaceous Freshwater Vegetation	Herbaceous Freshwater Vegetation	45	45	45	45	45	...	yes	yes	yes	yes	yes	yes	Terralink	2004-06-30T00:00:00	lcdb1000064420	POLYGON ((1722978.298 5972982.261, 1722971.518...
159353	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	Indigenous Forest	69	69	69	69	69	...	no	yes	yes	yes	yes	yes	Landcare Research	2004-06-30T00:00:00	lcdb1000166872	POLYGON ((1746450.557 5966033.058, 1746439.160...
17374	Mangrove	Mangrove	Mangrove	Mangrove	Mangrove	70	70	70	70	70	...	yes	yes	yes	yes	yes	yes	Landcare Research	2014-06-30T00:00:00	lcdb1000424037	POLYGON ((1751011.045 5917221.658, 1750977.402...